There was a lot of news about Spark’s ascension in the big data ranks this week, as well as some speculation. According to Cloudera’s Mike Olson, his company is widely embracing Spark — including to run Hive — but not in place of Impala.

Big data startup Databricks keeps humming along, announcing on Monday a large round of venture capital and a new cloud service that aims to seed adoption of Spark — a framework it says is faster, easier and more versatile than other options.

Databricks, the company behind the commercialization of the Apache Spark data-processing framework, is certifying third-party software to run on the platform. Spark is gaining popularity as a faster, easier alternative to MapReduce in Hadoop environments.

Sprint’s family plan doesn’t let members share data, texts or minutes. Instead they share a collective discount that grows the more members join. Sprint also expanded its new Spark LTE network to six more markets.

http://www.bigdatarepublic.com/author.asp?section_id=2840&doc_id=269178 This is a pretty interesting benchmark study, although the headline is a bit misleading because Hadoop isn’t really optimized for graph…

Cloudera has partnered with a startup called Databricks to integrate and support the Apache Spark data-processing platform within Cloudera’s Hadoop software. Spark, which is designed for speed and usability, is one of several technologies pushing Hadoop beyond MapReduce.

A team of professors behind the open source Spark and Shark in-memory big data projects has raised $13.9 million to commercialize the products via a company called Databricks. Spark and Shark are designed to be much faster and more flexible than Hadoop MapReduce and Hive.